An alternative quasi likelihood approach, Bayesian analysis and data-based inference for model specification

2014 ◽  
Vol 178 ◽  
pp. 132-145 ◽  
Author(s):  
Jae-Young Kim
Keyword(s):  
Bayesian Analysis ◽  
Model Specification ◽  
Likelihood Approach

scholarly journals An Analysis of Cepheid Distances Using Bayesian Statistics

2000 ◽  
Vol 176 ◽  
pp. 228-228
Author(s):  
Thomas G. Barnes ◽  
William H. Jefferys
Keyword(s):  
Bayesian Analysis ◽  
Surface Brightness ◽  
Radial Velocity Curve ◽  
Objective Model ◽  
Likelihood Approach ◽  
Selection For ◽  
Absolute Magnitudes ◽  
The Individual ◽  
Fully Bayesian ◽  
Selection Of

We have applied an approximately Bayesian and a fully Bayesian analysis to the calculation of Cepheid distances, radii and absolute magnitudes using the surface brightness (Baade–Wesselink) method. Both methods successfully account for errors in the data, provide unbiased distance estimates, and provide objective model selection for the radial velocity curve. In addition, the fully Bayesian analysis objectively selects a model for the magnitude curve; averages over models of various Fourier orders, properly weighted by the posterior probabilities of the individual models; and includes a Lutz–Kelker correction.The approximately Bayesian method is that described by Jefferys & Barnes (1999) and Barnes & Jefferys (1999). It is a maximum likelihood approach with objective selection of the order of the Fourier series model of the radial velocities.

A robust likelihood approach to inference about the difference between two multinomial distributions in paired designs

2017 ◽  
Vol 27 (10) ◽  
pp. 3077-3091 ◽  
Author(s):  
Tsung-Shan Tsou
Keyword(s):  
Binary Data ◽  
Pair Correlation ◽  
Real Data ◽  
Model Specification ◽  
Score Statistic ◽  
Parallel Design ◽  
Probability Structure ◽  
Likelihood Approach ◽  
The Difference ◽  
Robust Likelihood

Pairing serves as a way of lessening heterogeneity but pays the price of introducing more parameters to the model. This complicates the probability structure and makes inference more intricate. We employ the simpler structure of the parallel design to develop a robust score statistic for testing the equality of two multinomial distributions in paired designs. This test incorporates the within-pair correlation in a data-driven manner without a full model specification. In the paired binary data scenario, the robust score statistic turns out to be the McNemar’s test. We provide simulations and real data analysis to demonstrate the advantage of the robust procedure.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

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